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Communication Dans Un Congrès Année : 2000

Entropy-based optimisation for binary detection networks

Résumé

This contribution deals with the binary detection networks optimization using an entropy based criterion. The optimization of a detection elementary component consists in applying a variable threshold on the likelihood ratio, which depends on a posteriori probabilities. A gradient algorithm is proposed in order to find this threshold. The optimization results of the detection elementary component using entropy and Bayes' criteria are compared: the proposed approach has a very interesting property of robustness with respect to rare events, or with respect to events for which a priori probabilities are uncertain. In particular, the obtained ROC curve does not recede from the ideal point.
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Dates et versions

hal-01509844 , version 1 (18-04-2017)

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Denis Pomorski. Entropy-based optimisation for binary detection networks. The 3rd IEEE International Conference on Information Fusion (FUSION’2000), Jul 2000, Paris, France. ⟨10.1109/IFIC.2000.859895⟩. ⟨hal-01509844⟩

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